Computational Intelligent Design

Human has the remarkable capability to make best decisions in ample environmental information. The research presented here concerns establishing computational models that simulate the human abstraction, reasoning and creation capabilities during architectural design. This is important for two reasons. The first aspect is that the computational models permit to better understand the processes occurring via human mind, so that a deeper understanding of what design is and how it works is gained. The second aspect is that it permits to support a human decision-maker by means of powerful, ‘wise’ assistance during difficult tasks that are beyond human comprehension. In particular decisions in design and engineering are difficult to take due to increasing complexity that generally arises from the following three issues:

The softness, which stems from the need to represent many detailed features of an environment by means of a few quantities, so that models involve many non-linear relations among variables.

Optimization with the involvement of multiple, and stiff constraints that must be satisfied.

Involvement of several independent variables constituting a solution, which implies an excessive amount of possible solutions to be investigated within a limited time.

These issues make it formidably challenging to reach most suitable solutions. It is emphasized that this difficulty is alleviated when advanced computational methods are used to deal with the complexity, which is the subject matter of the computational intelligence-based work presented here. In particular methods from the domain of computational intelligence, such as evolutionary, neural and fuzzy computation, are employed to deal with soft and conflicting objectives, stiff constraints and vast solution domains. As result, solutions are guaranteed to satisfy the objectives at hand, while they satify the constraints at the same time. This quality assurance is highly desirable in the face of depleting resources and increasing demands imposed on engineering and design products, and it will become more and more relevant in the future, in proportion with the increase in complexity of the real-world design and decision-making problems.

Multi-dimensional performance analysis

Evaluation of the a decision needs consideration of many facts at the same time. A multi-dimensional analysis model is a model to compute the suitability of a decision, where every dimension refers to a certain decision aspect. Using such a model the suitability of a decision is computed with respect to multiple dimensions at the same time. A method to establish such a model is a neuro fuzzy modeling.

In a neuro fuzzy system the linguistic concepts involved in the decision analysis, such as sustainability, functionality, etc. are represented by means of neurons performing a non-linear mapping from the neuron's input to its output, simulating a reasoning activity in brain.The neuro fuzzy system we developed for decision analysis is different from artificial neural networks in the sense that the latter are established using training data to optimize the model parameters being the weight connections among nodes, whereas the method we developed optimizes the activation function in the neuron while the connections weights are fixed. This way our method is able to model deep knowledge from already existing knowledge, whereas ANN identify knoweldge from data, i.e. ANN are used for data-driven knowedge modelling, while fuzzy neural trees are used for knoweldge-driven knowledge modelling.

Illustration of fuzzy information processing at a neuron to obtain the design performance.

This way design parameters are incorporated into a complex algorithm, namely evolutionary algorithm with fuzzy neural computation, that finds the best set of solutions to meet the objectives set by the design team.

Visual representation of optimal solutions for two objectives in the urban design

The solutions obtained in this way are known as Pareto-optimal front. They provide a variety of outstanding alternatives to a decision maker, since none of these solutions is outperformed by another one. Every solution is equally valid, and a decision maker selects among them with great confidence.

Visual representation of optimal solutions for four objectives in the interior design task

As a computational solution set has been built, alternate designs are explored by varying the parameters. The generative system can handle effectively up to five objectives, and has no restriction regarding the number of variables playing role on the objectives. The amount of variables characterizing a solution is only limited by available computational time and power. For more than four objectives the five-dimensional Pareto front can be represented as well.

Visual representation of optimal solutions for five objectives

The strength of the approach is that solutions can be assessed without any presupposition, and confidence of finding the best solution is increased. Human and computational cognitive system are in an interaction loop: Human decision maker is setting the criteria, computations privide optimal solutions for these, based on these solutions the decision maker modifies criteria and so on, until a Pareto-optimal solution matches the designer's complete preferences as far as possible.

Two Pareto optimal solutions generated by a multi-dimensional performance-based design system

Perception modeling

Architectural design involves perception-based requirements, such as visual openness or visual privacy. Such requirements are challenging to treat, because the human vision process is highly complex, involving brain processes. Therefore the comparison of perceptual properties among scenes is imprecise.

To let perception play a more prominent role in design, a model of human vision is developed. The model is based on probabilistic terms. This way the complexity of the vision process is absorbed.

Unbiased visual attention for a nearby object

Unbiased visual attention for a distant object

The model is implemented by means of an avatar in virtual reality. The avatar experiences the environment in a human-like manner, so that the results are used during the evaluation of design alternatives.